• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.5
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• pp.2555-2575
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• 2017

This paper presents a dual exposure fusion method for image enhancement. Images taken with a short exposure time usually contain a sharp structure, but they are dark and are prone to be contaminated by noise. In contrast, long-exposure images are bright and noise-free, but usually suffer from blurring artifacts. Thus, we fuse the dual exposures to generate an enhanced image that is well-exposed, noise-free, and blur-free. To this end, we present a new scale-space patch-match method to find correspondences between the short and long exposures so that proper color components can be combined within a proposed dual non-local (DNL) means framework. We also present a residual filtering method that eliminates the structure component in the estimated noise image in order to obtain a sharper and further enhanced image. To this end, the entropy is utilized to determine the proper size of the filtering window. Experimental results show that our method generates ghost-free, noise-free, and blur-free enhanced images from the short and long exposure pairs for various dynamic scenes.

• 한국지구물리탐사학회:학술대회논문집
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• 2006.06a
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• pp.155-160
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• 2006

In this paper, we propose a spatial filtering scheme using factorial kriging, one of geostatistical filtering methodin order to separate regional and residual gravity anomaly. This scheme is based on the assumption that regional anomalies have longer distance relation and residual anomalies have effected on smaller range. We decomposed gravity anomalies intotwo variogram models with long and short effectiveranges by means of factorial kriging. And decomposed variogram models produced the regional and residual anomalies. This algorithm was examined using by a synthetic gravity data, and applied to a real microgravity data to figure out abandoned mineshaft.

• Current Optics and Photonics
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• v.7 no.4
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• pp.408-418
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• 2023

Due to the technological limitations of infrared thermography, infrared focal plane array (IFPA) imaging exhibits stripe non-uniformity, which is typically fixed pattern noise that changes over time and temperature on top of existing non-uniformities. This paper proposes a stripe non-uniformity correction algorithm based on scene-adaptive nonlinear filtering. The algorithm first uses a nonlinear filter to remove single-column non-uniformities and calculates the actual residual with respect to the original image. Then, the current residual is obtained by using the predicted residual from the previous frame and the actual residual. Finally, we adaptively calculate the gain and bias coefficients according to global motion parameters to reduce artifacts. Experimental results show that the proposed algorithm protects image edges to a certain extent, converges fast, has high quality, and effectively removes column stripes and non-uniform random noise compared to other adaptive correction algorithms.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.6
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• pp.79-84
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• 2015

In a distribution of digital image, there is a serious problem that is a distribution of the altered image by a forger. For the problem solution, this paper proposes a median filtering (MF) image forensic decision algorithm using a feature vector according to the pixel value's gradients. In the proposed algorithm, AR (Autoregressive) coefficients are computed from pixel value' gradients of original image then 1th~6th order coefficients to be six feature vector. And the reconstructed image is produced by the solution of Poisson's equation with the gradients. From the difference image between original and its reconstructed image, four feature vector (Average value, Max. value and the coordinate i,j of Max. value) is extracted. Subsequently, Two kinds of the feature vector combined to 10 Dim. feature vector that is used in the learning of a SVM (Support Vector Machine) classification for MF (Median Filtering) detector of the altered image. On the proposed algorithm of the median filtering detection, compare to MFR (Median Filter Residual) scheme that had the same 10 Dim. feature vectors, the performance is excellent at Unaltered, Averaging filtering ($3{\times}3$) and JPEG (QF=90) images, and less at Gaussian filtering ($3{\times}3$) image. However, in the measured performances of all items, AUC (Area Under Curve) by the sensitivity and 1-specificity is approached to 1. Thus, it is confirmed that the grade evaluation of the proposed algorithm is 'Excellent (A)'.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.4
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• pp.195-201
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• 2015

For median filtering (MF) detection in altered digital images, this paper presents a new feature vector that is formed from autoregressive (AR) coefficients via an AR model of the gradients between the neighboring row and column lines in an image. Subsequently, the defined 10-D feature vector is trained in a support vector machine (SVM) for MF detection among forged images. The MF classification is compared to the median filter residual (MFR) scheme that had the same 10-D feature vector. In the experiment, three kinds of test items are area under receiver operating characteristic (ROC) curve (AUC), classification ratio, and minimal average decision error. The performance is excellent for unaltered (ORI) or once-altered images, such as $3{\times}3$ average filtering (AVE3), QF=90 JPEG (JPG90), 90% down, and 110% up to scale (DN0.9 and Up1.1) images, versus $3{\times}3$ and $5{\times}5$ median filtering (MF3 and MF5, respectively) and MF3 and MF5 composite images (MF35). When the forged image was post-altered with AVE3, DN0.9, UP1.1 and JPG70 after MF3, MF5 and MF35, the performance of the proposed scheme is lower than the MFR scheme. In particular, the feature vector in this paper has a superior classification ratio compared to AVE3. However, in the measured performances with unaltered, once-altered and post-altered images versus MF3, MF5 and MF35, the resultant AUC by 'sensitivity' (TP: true positive rate) and '1-specificity' (FN: false negative rate) is achieved closer to 1. Thus, it is confirmed that the grade evaluation of the proposed scheme can be rated as 'Excellent (A)'.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.8
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• pp.67-73
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• 2015

In a distribution of digital image, there is a serious problem that is the image alteration by a forger. For the problem solution, this paper proposes the forensic decision algorithm of a median filtering (MF) image using the feature vector based on a coefficient of variation (c.v.) of Fourier transform. In the proposed algorithm, we compute Fourier transform (FT) coefficients of row and column line respectively of an image first, then c.v. between neighboring lines is computed. Subsquently, 10 Dim. feature vector is defined for the MF detection. On the experiment of MF detection, the proposed scheme is compared to MFR (Median Filter Residual) and Rhee's MF detection schemes that have the same 10 Dim. feature vector both. As a result, the performance is excellent at Unaltered, JPEG (QF=90), Down scaling (0.9) and Up scaling (1.1) images, and it showed good performance at Gaussian filtering ($3{\times}3$) image. However, in the performance evaluation of all measured items of the proposed scheme, AUC (Area Under ROC (Receiver Operating Characteristic) Curve) by the sensitivity and 1-specificity approached to 1 thus, it is confirmed that the grade of the performance evaluation is rated as 'Excellent (A)'.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.44
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• pp.243-255
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• 1997

Forecasting the future values of an observed time series is an important problem in many areas, including economics, traffic engineering, production planning, sales forecasting, and stock control. The purpose of this paper is aimed to discover the more efficient forecasting model through the parameter estimation and residual analysis among the quantitative method such as Winters' exponential smoothing model, Box-Jenkins' model, and Kalman filtering model. The mean of the time series is assumed to be a linear combination of known functions. For a parameter estimation and residual analysis, Winters', Box-Jenkins' model use Statgrap and Timeslab software, and Kalman filtering utilizes Fortran language. Therefore, this paper can be used in real fields to obtain the most effective forecasting model.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.888-890
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• 2003

The objective of this investigation is to establish a simple yet effective block adjustment procedure for the orthorectification of multi-orbit satellite images. The major works of the proposed scheme are: (1) adjustment of satellite‘s orbit accurately, (2) calculation of the error vectors for each tie point using digital terrain model and ray tracing technique, (3) refining the orbit using the Least Squares Filtering technique and (4) generation of the orthophotos. In the process of least squares filtering, we use the residual vectors on ground control points and tie points to collocate the orbit. In orthorectification, we use the indirect method to generate the orthoimage. Test areas cover northern Taiwan. Test images are from SPOT 5 satellite. Experimental results indicate that proposed method improves the relative accuracy significantly.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.5
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• pp.42-47
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• 2017

In the distribution of digital image, the median filtering is used for a forgery. This paper proposed the algorithm of a image forensics detection for the classification of median filtering. For the solution of this grave problem, the feature vector is composed of 42-Dim. The detected quantity 32, 64 and 128 of forgery image edges, respectively, which are processed by the Hough transform, then it extracted from the start-end point coordinates of the Hough Lines. Also, the Hough Peaks of the Angle-Distance plane are extracted. Subsequently, both of the feature vectors are composed of the proposed scheme. The defined 42-Dim. feature vector is trained in SVM (Support Vector Machine) classifier for the MF classification of the forged images. The experimental results of the proposed MF detection algorithm is compared between the 10-Dim. MFR and the 686-Dim. SPAM. It confirmed that the MF forensic classification ratio of the evaluated performance is 99% above with the whole test image types: the unaltered, the average filtering ($3{\times}3$), the JPEG (QF=90 and 70)) compression, the Gaussian filtered ($3{\times}3$ and $5{\times}5$) images, respectively.

• Journal of Korean Society for Atmospheric Environment
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• v.11 no.4
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• pp.361-367
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• 1995

This study has been carried out to investigate the filteration performance of Electrostatically Stimulated Fabric Filter(ESFF) at high temperature condition. The electric field was maintained parallel to the fabric surface. The benefits of ESFF are lower residual pressure drop, improvement of fine particle removal efficiency and increasing reduced rate of pressure drop during a filteration cycle, stable operation at higher filtering velocities. According to the variance of filtering velocities and dust loadings, the results are summarized as follows; By imposing an electric field on the filter, the reduced rate of pressure drop was 7.sim.18% at room temperature, and when filtering velocity was 1.8m/min and dust loading was 1g/m$^{3}$, the value of reduced rate of pressure drop was shown the highest. Under the electric field around the filter, the reduced rate of pressure drop was 10.sim.35% at high temperature, and when filtering velocity was 1.8m/min and dust loading was 1g/m$^{3}$, the value of reduced rate of pressure drop was shown the highest. Most of all, at high temperature, the value of reduced rate of pressure drop was resulted to 25%. Also the collecting efficiency was shown clearly improved. By the SEM photo analysis, the number of penetrated particles at the Conventional Fabric Filter was approximately two times that of Electrostatically Stimulated Fabric Filter.